In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium – 100 Years ISPRS, Vienna, Austria, July 5–7, 2010, IAPRS, Vol. XXXVIII, Part 7B
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THE CAPABILITIES OF TERRASAR-X IMAGERY FOR RETRIEVAL OF
FOREST PARAMETERS
Roland Perko, Hannes Raggam, Karlheinz Gutjahr and Mathias Schardt
Institute of Digital Image Processing, Joanneum Research, Graz, Austria
{roland.perko,hannes.raggam,karlheinz.gutjahr,mathias.schardt}@joanneum.at
Technical Commission VII Symposium 2010
KEY WORDS: Forestry, Mapping, Photogrammetry, Classification, DEM/DTM, SAR, High resolution.
ABSTRACT:
The TerraSAR-X mission was launched in June 2007 operating a very high resolution X-band SAR sensor. In Spotlight mode images
are collected with 0.75m GSD and also at various look angles. The presented paper reports methodologies, algorithms and results
emerged from the Austrian research project “Advanced Tools for TerraSAR-X Applications in GMES” with emphasis on retrieval of
forest parameters. For deriving forest features like crown closure, vertical stand structure or stand height a digital forest canopy model
serves as an important source of information. The procedures to be applied cover advanced stereo-radargrammetric and interferometric
data processing, as well as image segmentation and image classification. A core development is the multi-image matching concept
for digital surface modelling based on geometrically constrained matching, extending the standard stereo-radargrammetric approach.
Validation of surface models generated in this way is made through comparison with LiDAR data, resulting in a standard deviation
height error of less than 2 meters over forest. Image classification of forest regions is then based on TerraSAR-X backscatter information
(intensity and texture), a 3D canopy height model and interferometric coherence information yielding a classification accuracy above
90%. Such information is then directly utilized to extract forest border lines. Overall, the TerraSAR-X sensor delivers imagery that
can be used to automatically retrieve forest parameters on a large scale, being independent of weather conditions which often cause
problems for optical sensors due to cloud coverage.
1
INTRODUCTION
TerraSAR-X is the first German satellite out of a public private
partnership (PPP) between German Aerospace Center (DLR)
and Astrium GmbH and was launched in June 2007. The
novel X-band SAR sensor can acquire image products in
Spotlight, Stripmap and ScanSAR mode at very high resolutions
down to 0.75m (Eineder et al., 2008). One main aspect of the
Austrian research project “Advanced Tools for TerraSAR-X
Applications in GMES” (AT-X) dealt with the derivation of
forest related parameters using TerraSAR-X imagery. The first
part consists of precise image matching of such imagery for fully
automatic derivation of digital surface models (DSM) which are
subsequently used to derive a canopy height models (CHM).
The second part concerns image classification with the focus on
distinguishing forest from non-forest regions.
2
OUR METHODS
The big picture of our workflow is sketched in Figure 1. As seen,
a DSM is extracted using multi-image radargrammetry. This
DSM is utilized together with InSAR products and backscatter
information to derive a forest classification. Finally, this
segmentation helps to correct the height of canopy regions
resulting in the final corrected DSM.
2.1
Multi-Image DSM Generation
The accurate 3D reconstruction of timbered regions using
TerraSAR-X imagery alone is very challenging due to two
reasons. First, the traditional InSAR-based processing does
not yield appropriate results over forest as the InSAR phase
decorrelates within the 11 days TerraSAR-X repeat cycle
(Bamler et al., 2008). Second, even in cases of temporal
phase correlation the resulting canopy height is systematically
452
Figure 1: Proposed workflow for deriving forest parameters using
TerraSAR-X data.
underestimated. The reason for that is the fact, that the SAR
signal in X-band penetrates into the forest canopy changing the
InSAR phase center and therefore the reconstructed height.
This aspect has been observed on InSAR-based processing of
airborne X-band data (Izzawati et al., 2006, Tighe et al., 2009).
To tackle all these difficulties we first derive digital surface
models using a multi-image stereo-radargrammetric approach.
Then, the resulting DSMs are corrected (undoing the canopy
height underestimation) by applying an empirically learned
correction model on regions of forest.
The radargrammetric processing is described in detail in
(Raggam et al., 2010a) and can be applied successfully due to
the very exact pointing accuracy of the TerraSAR-X sensor
(Bresnahan, 2009, Raggam et al., 2010b). The main steps in
the DSM extraction are pairwise stereo matching followed by
a joint point intersection procedure. To get robust matching
results image triplets are used, i.e. three TerraSAR-X images
acquired under different look angles. The main point is, that
adjacent images (similar look angles) provide good matching,
however unfavorably geometric properties. Therefore, for
triplets image 1 can be matched to image 2 and image 2 to image
3. Thus, points from image 1 are transferred to image 3 yielding
a large intersection angle and therefore a more robust result. In
addition image 1 and image 3 are directly matched resulting in
In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium – 100 Years ISPRS, Vienna, Austria, July 5–7, 2010, IAPRS, Vol. XXXVIII, Part 7B
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Author Index
over-determination in the spatial point intersection.
Stereo matching of a TerraSAR-X image pair is improved by
including geometric constraints. First, one image is pseudo
epipolar registered based on an affine polynomial transformation
using both sensor models. Second, in image matching a starting
location for each pixel is predicted, again using sensor models
and a coarse DSM (SRTM or ASTER model).
The presented approach yields an areal digital surface model.
When subtracting a reference digital terrain model (DTM),
e.g. available from airborne laser scanning, a canopy height
model (CHM) can be extracted (cf. Figure 2 and Eq. (1)). Such
CHMs serve as an important information for the retrieval
of forest parameters.
As mentioned before, the canopy
height underestimation can be quantified using laser scanner
ground truth data. Such comparison enables to determine the
underestimation factor τ in percent. In regions of forest the
TerraSAR-X DSM is then corrected by multiplication with the
factor 1/(1 − τ /100). The forest segmentation presented in the
next section is then used to correct the canopy height bias (see
Figure 1). It should be noted that this problem is not straight
forward, as such underlying image segmentation often is just not
available.
Figure 2: Explanation of relation between digital surface model
(DSM), digital terrain model (DTM) and canopy height model
(CHM).
CHM = DSM − DTM
2.2
(1)
Forest Segmentation
The proposed forest segmentation should allow separating
regions of forest from non-forest areas. Recently, first results on
this topic were published in (Breidenbach et al., 2009). They
perform the classification on TerraSAR-X backscatter mean and
standard deviation statistics alone. We extend their method by
including backscatter intensity and texture information, a 3D
canopy height model and interferometric coherence information.
For classification a supervised approach is chosen by selecting
multiple regions together with their ground truth class labels
(forest / non-forest) and training a maximum likelihood
classifier. This classifier is then applied to the whole spatial
extent of given images.
The resulting classification is
constructed with a GSD of 5 meters. Next, very small areas are
rejected based on a region labeling approach.
Texture Description. As observed in (Breidenbach et al., 2009)
regions of vegetation are less textured, i.e. more homogenous,
than regions of settlements or agricultural areas. (Haack et al.,
2000) suggest to describe this texture information by a variance
filter. However, our tests showed that such simple parameter is
not working satisfactorily on TerraSAR-X data. Therefore, we
choose the Texture-transform (Tavakoli Targhi et al., 2006) which
is invariant to illumination, computationally simple and easy to
parameterize so that it also performs reasonably on high resolution radar data. This transform can be seen as a spatial frequency
analysis, where the key idea is to investigate the singular values of
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Keyword Index
matrices formed directly from gray values of local image patches
(the backscatter information in our case). More specifically, the
gray values of a square patch around a pixel are put into a matrix of the same size as the original patch. The texture descriptor
is computed as the sum of some singular values of this matrix.
The largest singular value encodes the average brightness of the
patch and is thus not useful as a texture description. However, the
smaller singular values encode high frequency variations characteristics of visual texture. Therefore, the singular values of this
matrix are sorted in decreasing order. Then the Texture-transform
at each pixel is defined as the sum of the smallest singular values. For the tests several window sizes and singular values ranges
were chosen, where a window of size 33 × 33 and a range of 20
to 33 smallest singular values performed best.
Canopy Height Model. Obviously, vegetation heights are a useful information to segment regions of forest. The canopy height
model is extracted employing the methodology described in Section 2.1.
InSAR Coherence. For forest segmentation the interferometric
coherence, which is a measure of the interferogram’s quality,
can be of great value since regions of vegetation suffer
from temporal decorrelation (see also the detailed study
on interferometric decorrelation (Zebker and Villasenor,
1992)). The standard coherence estimation is based on a local
complex cross-correlation and is known to over-estimate
the real coherence value. In general, a larger window within
cross-correlation provides a better coherence estimate. At the
time of radar sensors like ERS the standard procedure was
to estimate the coherence over the same window used for
multi looking. As the multi looking sizes become smaller for
TerraSAR-X imagery the coherence was highly over-estimated
resulting in a noisy coherence image. Therefore, a decoupling
of the window size of multi looking and cross-correlation is
introduced. The resulting coherence estimate uses a correlation
window of 10 × 10 pixel and a multi looking window of 2 × 3
pixel (azimuth × range). Regions of very low coherence
correspond mainly to vegetation (forests and agricultural areas).
Thus, such coherence information is used in the classification
process as one feature.
3
TEST DATA
Within the AT-X project the proposed algorithms have been applied to several test sites. For the presented study only a single
test site called “Burgau” is chosen to keep the results clearly arranged. The test site of interest spans an area of 12 × 12 km2
in Austria. This rural test area covers agricultural as well as forest areas and shows flat to slightly hilly terrain, the ellipsoidal
heights ranging from 270 to 445 meters above sea level (cf. Figure 3). The forested regions in the area mainly consists of dense
stands of deciduous trees.
Multi-Image DSM Generation.
For multi-image DSM
derivation the test data consist of multiple TerraSAR-X
multi-look ground range detected (MGD) Spotlight products
from ascending, respectively descending, orbit. All images were
ordered as single-polarization products (HH) with science orbit
accuracy and were acquired in the period of July and August
2009. Table 1 reports the major parameters of the “Burgau” test
site. It should be noted that the images acquired at steep look
angles (i.e. MGD asc1 and MGD dsc1) have a lower GSD than
all other products.
In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium – 100 Years ISPRS, Vienna, Austria, July 5–7, 2010, IAPRS, Vol. XXXVIII, Part 7B
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4
Figure 3: Overview of the test site “Burgau”. This topographic
map shows regions of forest in green color.
Name
MGD asc1
MGD asc2
MGD asc3
MGD dsc1
MGD dsc2
MGD dsc3
Look angle
22.3◦
37.2◦
48.5◦
21.3◦
36.5◦
48.0◦
GSD
1.25m
0.75m
0.75m
1.25m
0.75m
0.75m
Date
2009-07-28
2009-08-02
2009-08-07
2009-07-30
2009-08-05
2009-07-31
Forest Segmentation. The InSAR coherence used in the
segmentation process is derived from a TerraSAR-X single look
complex (SSC) InSAR pair (see Table 2). These images were
ordered as dual-polarization products (HH,VV) with science
orbit accuracy and were acquired in March 2008.
Name
SSC dsc1
SSC dsc2
Look angle
36.0◦
36.0◦
Multi-Image DSM Generation. For visual interpretation some
detailed results are shown in Figure 4. All results are given with
a GSD of 2 meters in UTM33 projection. Figure 4(a) shows
the TerraSAR-X DSM, (b) the LiDAR reference DTM, (c) the
TerraSAR-X and LiDAR based CHM, (d) the pure LiDAR CHM,
(e) the TerraSAR-X based height error (i.e. the ground truth
LiDAR DSM subtracted from the TerraSAR-X derived DSM)
and (d) a topographic map. The TerraSAR-X CHM corresponds
visually very well to the LiDAR CHM and to the topographic
map, however the TerraSAR-X DSM is too low over forest, as
seen in Figure 4(e). Regions of bluish color indicate height underestimations and such regions are placed in forests or on forest borders. The quantitative accuracy analysis is listed in Table 3. Heights over bare ground are reconstructed with very high
accuracy (mean value below 20 cm and standard deviation of
about 2 meters). The canopy height however is systematically
underestimated by approximately 27.5% for this test site. In our
previous work we estimated an average underestimation using
Spotlight and Stripmap imagery and multiple scenes which was
26.6%±1.4% (Perko et al., 2010). When correcting the height
bias with this learned value the reconstruction over forest becomes a lot better. In particular it decreases to a residual height
error of 20 cm, like on bare ground (see Table 3 bottom). Figure 5
and Figure 6 show detailed analyses of the canopy height underestimation w.r.t. the canopy height before and after the discussed
correction, clarifying that the underestimation over forest can be
corrected for this test site.
asc
dsc
Table 1: Detailed parameters for the Spotlight images.
Date
2008-03-17
2008-03-28
Table 2: Detailed parameters for the Spotlight InSAR pair.
Reference Data. To enable quantitative evaluations LiDAR data
is used as ground truth information. The LiDAR reference
data covers four measurements per square meter, which are
processed to highly accurate DSMs and DTMs. While the DSMs
are automatically extracted, the DTMs are semi-automatically
generated by classifying regions of vegetation, building, bridges
and other man-made structures.
To evaluate the DSMs derived using TerraSAR-X multi-image
radargrammetry, 30 regions on bare ground and 70 regions in
forest are selected. The average residual height error over such
regions describes the DSM quality. Over forest, the average
canopy height underestimation τ is extracted and used to correct
the canopy height.
To evaluate the image segmentation quality a ground truth
reference mask is derived using LiDAR data. The 1m GSD
LiDAR CHM is filtered with an order-statistic filter of size 7 × 7
and order 37, i.e. the 75th percentile. The CHM is then down
sampled to a GSD of 5m using a 5 × 5 average resampling.
Next, pixels with a height larger than 8 meters are considered as
forest regions and small regions are filled to eliminate noise.
454
RESULTS AND DISCUSSION
bare ground
µ [m]
σ [m]
0.07
2.04
0.18
1.90
asc
dsc
forest
µ [m]
σ [m]
τ [%]
-5.81
1.87
28.5
-5.45
1.83
26.4
forest after height correction
0.22
1.87
-1.16
0.08
1.83
-0.42
Table 3: Detailed 3D height analysis of the TerraSAR-X derived
DSMs.
Forest Segmentation. The features used for forest segmentation are shown in Figure 7. Obviously, the most important information for the segmentation are the InSAR coherence and the
canopy height model. The confusion matrix in Table 4 reveals
that 90% of pixels (here one pixel has a GSD of 5 meters) are
correctly classified. About 8% of non-forest regions are classified incorrectly as forest. This especially happens in small forest
clearances which are not seen due to the slant range SAR geometry or which result from image matching failures. The 2% of pixels classified wrongly as non-forest are mainly small forest stands
where image matching is unsuccessful and thus such regions get
interpolated. In addition it should be noted that this evaluation is
relative to the LiDAR ground truth segmentation. Therefore, the
achieved accuracy is most likely above the 90% since some artifacts exist in the LiDAR model. For instance some power supply
lines are classified as forest. In comparison to the state-of-the-art
classification of TerraSAR-X data in (Breidenbach et al., 2009)
the proposed method performs very well. On first glance their
method also reaches a classification accuracy of 90%. However,
the evaluation is based on image blocks with 20 × 20 m2 . When
reducing the GSD to 5 meters, like in our study, the classification
accuracy of (Breidenbach et al., 2009) drops to 72.5%. Obviously, our method performs better as a diversity of information
like canopy height model, coherence or texture descriptors is incorporated into the classification process.
In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium – 100 Years ISPRS, Vienna, Austria, July 5–7, 2010, IAPRS, Vol. XXXVIII, Part 7B
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(a) TerraSAR-X DSM
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(b) LiDAR DTM
Figure 6: Canopy height underestimation for the ascending triplet
after the proposed height correction.
(c) TerraSAR-X CHM
(e) TerraSAR-X DSM error
(d) LiDAR CHM
(a) backscatter
(b) coherence
(c) texture
(d) canopy height model
(e) ground truth segmentation
(f) TerraSAR-X segmentation
(f) topographic map
Figure 4: Exemplary results of DSM and CHM extraction. The
TerraSAR-X DSM (a), LiDAR reference DTM (b), TerraSARX CHM (c), LiDAR CHM (d), color coded TerraSAR-X height
error (e), and a topographic map for visual comparison (f). A
subset of 7.1 × 7.6 km2 is shown.
Figure 5: Canopy height underestimation for the ascending triplet
before the proposed height correction.
455
Figure 7: Exemplary input data used for image segmentation (ad), ground truth segmentation based on laser scanner vegetation
height model (e) and TerraSAR-X based segmentation (f).
In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium – 100 Years ISPRS, Vienna, Austria, July 5–7, 2010, IAPRS, Vol. XXXVIII, Part 7B
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correct: 90.37%
estimation
forest
non-forest
ground truth
forest
non-forest
40.01%
7.68%
1.96%
50.36%
Keyword Index
will be larger for coniferous trees and for clearer stands.
Future work should focus on a comparison to TanDEM-X DSMs
compromising multiple InSAR pairs with different look angles
to further understand the penetration into the canopy of X-band
signals.
Table 4: Confusion matrix of forest segmentation.
Forest Border Lines. Finally, forest border lines can directly be
extracted from the segmentation result via edge detection. Figure 8 shows some examples. Overall, the quality of extracted
forest border lines is higher for huge dense forests than for small
isolated stands (this aspect was also observed in (Breidenbach et
al., 2009)), where small stands are often not detected at all. Nevertheless, forest border lines are in general very well extracted
and their accuracy is directly dependent on the forest segmentation.
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5
CONCLUSIONS AND FUTURE WORK
TerraSAR-X imagery enables the retrieval of certain forest
parameters.
In particular, multiple TerraSAR-X images
representing the same area on ground under different look angles
can be used to fully automatically derive accurate DSMs. In case
when reference DTMs are available the canopy height model
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